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--- |
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language: |
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- id |
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license: mit |
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base_model: indolem/indobert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment-pt-pl10-1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sentiment-pt-pl10-1 |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3011 |
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- Accuracy: 0.8972 |
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- Precision: 0.8754 |
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- Recall: 0.8773 |
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- F1: 0.8764 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 30 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5535 | 1.0 | 122 | 0.5078 | 0.7268 | 0.6606 | 0.6242 | 0.6327 | |
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| 0.4682 | 2.0 | 244 | 0.4185 | 0.8170 | 0.7798 | 0.7756 | 0.7776 | |
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| 0.3849 | 3.0 | 366 | 0.3809 | 0.8170 | 0.7968 | 0.7380 | 0.7573 | |
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| 0.3127 | 4.0 | 488 | 0.3280 | 0.8571 | 0.8266 | 0.8314 | 0.8289 | |
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| 0.2869 | 5.0 | 610 | 0.3169 | 0.8622 | 0.8333 | 0.8350 | 0.8341 | |
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| 0.274 | 6.0 | 732 | 0.3218 | 0.8772 | 0.8467 | 0.8731 | 0.8576 | |
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| 0.2539 | 7.0 | 854 | 0.3038 | 0.8672 | 0.8378 | 0.8460 | 0.8417 | |
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| 0.2286 | 8.0 | 976 | 0.3202 | 0.8672 | 0.8479 | 0.8235 | 0.8342 | |
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| 0.2249 | 9.0 | 1098 | 0.2973 | 0.8872 | 0.8606 | 0.8727 | 0.8662 | |
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| 0.2083 | 10.0 | 1220 | 0.3128 | 0.8722 | 0.8602 | 0.8221 | 0.8377 | |
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| 0.1935 | 11.0 | 1342 | 0.2957 | 0.8922 | 0.8665 | 0.8788 | 0.8722 | |
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| 0.1859 | 12.0 | 1464 | 0.2869 | 0.8822 | 0.8548 | 0.8667 | 0.8603 | |
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| 0.1735 | 13.0 | 1586 | 0.3061 | 0.8797 | 0.8633 | 0.8399 | 0.8502 | |
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| 0.1804 | 14.0 | 1708 | 0.2955 | 0.8897 | 0.8632 | 0.8770 | 0.8695 | |
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| 0.1628 | 15.0 | 1830 | 0.2973 | 0.8972 | 0.8767 | 0.8748 | 0.8757 | |
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| 0.1619 | 16.0 | 1952 | 0.3023 | 0.8897 | 0.8618 | 0.8820 | 0.8707 | |
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| 0.1514 | 17.0 | 2074 | 0.2997 | 0.8972 | 0.8732 | 0.8823 | 0.8776 | |
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| 0.1503 | 18.0 | 2196 | 0.3002 | 0.8947 | 0.8718 | 0.8755 | 0.8737 | |
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| 0.154 | 19.0 | 2318 | 0.3031 | 0.8947 | 0.8730 | 0.8730 | 0.8730 | |
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| 0.1408 | 20.0 | 2440 | 0.3011 | 0.8972 | 0.8754 | 0.8773 | 0.8764 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.2 |
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